Experiences of Secondary Long-term Occassional Teachers Seeking Permanent Employment in Ontario
Bibliographic record
Abstract
This study analyses how Long-term Occasional Teachers (LTO) interface with permanent teachers, students and the administration as they seek to gain permanent employment. It also focuses on Internationally Educated Teachers (IETs) who are LTOs as they seek integration into the Ontario teaching workforce as permanent teachers. The study utilized a general qualitative research methodology with interviews to obtain participant data and is undergird by notions of Post-Fordism, communities of practice and equity education. Data were collected from 15 participants who self-identified as LTOs. Of the 15 LTOs 4 identified as IETs who sought employment as full-time teachers in Ontario. Findings reveal that (a) LTOs and in particular those who were internationally trained (IETs) felt that they were required to continuously reinvent themselves to become marketable for the Ontario education system; (b) their knowledge seems to be less appreciated than that of permanent contract teachers and; (c) there are challenges achieving permanent employment. This study also reports on the insecurities and low self-esteem caused by extended periods of job search as well as the impact of different forms of discrimination on their ability to obtain full time employment. This study relies on the extant literature on the experiences of LTOs and IETs to build greater awareness of the challenges they experience while seeking employment in Ontario as full-time teachers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".